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287 results for “Ontology”
CROSSCULT user ontology
<p>CC-UserOntology is the user ontology to be used in the CROSSCULT project, aiming to capture rich information in user profiles to enable innovative applications in relation to cultural heritage reflection and re-interpretation.</p>
Intelligent Energy Systems Ontology: Local flexibility market and power system co-simulation demonstration
<p>The Intelligent Energy Systems Ontology (IESO) provides semantic interoperability within a society of multi-agent systems (MAS) developed in the scope of power and energy systems (PES). It leverages the knowledge from existing and publicly available semantic models developed for specific PES subdomains to accomplish a shared vocabulary among the agents of the MAS community, overcoming heterogeneity among the reused ontologies. IESO provides agents with semantic reasoning, constraints validation, and data uniformization. The use of IESO is demonstrated through the simulation of the management of a rural distribution network, considering the validation of the grid’s technical constraints. This dataset publishes files demonstrating: i) a snapshot of the initial semantic knowledge base (KB); ii) queries to the KB to get services inputs; iii) conversions between syntactic and semantic models; <br> iv) constraints validations; v) automatic conversion of units of measure.</p>
SAQI: An Ontology based Knowledge GraphPlatform for Social Air Quality Index
<p>This dataset consists of all contributions made by Social AQI (SAQI) project. The description of dataset is as below -</p> <p>Local Sensor Data (hyperlocal-air-quality-sensor-data) - contains all sensors values recorded through local neighbourhood sensors throught the length of the project <br> Locations for all these sensors are as below - In Najafgarh, Delhi, India : Jharoda Kalan, Nangli Dairy and DTC Bus terminal.<br> In Okhla : Sanjay Colony, Tekhand, Shaheen Bagh.</p> <p>Data from <a href="https://cpcb.nic.in/">Central Pollution Control Board</a> (central-air-quality-sensor-data) - Najafgarh_CPCB.csv, Okhla_CPCB.csv : Contains data provided by CPCB from Najafgarh,Delhi and Oklha, Delhi</p> <p><br> PollutionODP.owl : Ontology Design Pattern for pollution - http://ontologydesignpatterns.org/wiki/Submissions:Pollution.<br> <br> Ontology : SAQI ontology as triples (ttl), xml (rdf) and json-ld (json) serialization format<br> Ontology documentation : ontology/diagram contains figures describing ontology, ontology/documentation/saqi.html contains LODE documentation for the ontology</p> <p><br> ethnographic-survey-data - anonymized survey responses for initial pollution perception and literacy survey as well as SAQI app feedback survey.</p> <p>SHACL-shapes - for validating against SAQI ontology.</p> <p> sparql-queries - sample queries to run on our ontology.</p> <p>setup-rdf-store-script - script to setup rdf store with given data using rml mapper.</p> <p> </p> <p><br> </p>
Ontology Enrichment from Texts (OET): A Biomedical Dataset for Concept Discovery and Placement
<p>A biomedical dataset supporting ontology enrichment from texts, by concept discovery and placement, adapting the MedMentions dataset (PubMed abstracts) with SNOMED CT of versions in 2014 and 2017 under the Diseases (disorder) sub-category and the broader categories of Clinical finding, Procedure, and Pharmaceutical / biologic (CPP) product.</p> <p>The dataset is documented in the work, <em>Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and Placement</em>, on arXiv: <a href="https://arxiv.org/abs/2306.14704">https://arxiv.org/abs/2306.14704</a> (CIKM 2023). The companion code is available at https://github.com/KRR-Oxford/OET.</p> <p>Out-of-KB mention discovery (including the settings of mention-level data) is further partly documented in the work, <em>Reveal the Unknown: Out-of-Knowledge-Base Mention Discovery with Entity Linking</em>, on arXiv: <a href="https://arxiv.org/abs/2302.07189">https://arxiv.org/abs/2302.07189</a> (CIKM 2023).</p> <p>ver4: we made a version of mention-level data for out-of-KB discovery and concept placement separately: the former (for out-of-KB discovery) has out-of-KB mentions in training data, while the latter (for concept placement) has only out-of-KB mentions during the evaluation (validation and test) and not in the training data. Also, we split the original "test-NIL.jsonl" (now "test-NIL-all.jsonl") into "valid-NIL.jsonl" and "test-NIL.jsonl" for a better evaluation.</p> <p>ver3: we revised and updated mention-level data (syn_full, synonym augmentation setting) and the folder structure, and also updated the edge catalogues with complex edges.</p> <p>ver2: we revised the mention-level data by only keeping out-of-KB mentions (or "NIL" mentions) associated with one-hop edges (including leaf nodes, as <leaf node, NULL>) and two-hop edges in the ontology (SNOMED CT 20140901).</p> <p>Acknowledgement of data sources and tools below:</p> <p>* SNOMED CT https://www.nlm.nih.gov/healthit/snomedct/archive.html (and use snomed-owl-toolkit to form .owl files)<br>* UMLS https://www.nlm.nih.gov/research/umls/licensedcontent/umlsarchives04.html (and mainly use MRCONSO for mapping UMLS to SNOMED CT)<br>* MedMentions https://github.com/chanzuckerberg/MedMentions (source of entity linking)</p> <p>* Protégé http://protegeproject.github.io/protege/<br>* snomed-owl-toolkit https://github.com/IHTSDO/snomed-owl-toolkit<br>* DeepOnto https://github.com/KRR-Oxford/DeepOnto (based on OWLAPI https://owlapi.sourceforge.net/) for ontology processing and complex concept verbalisation</p>
SNIK Ontology
SNIK is an ontology of information management in hospitals that consists of a meta model and several subontologies.
"How much OWL do you need to know to make sense of building ontologies?" supplementary material
<p>This records contains the ontologies analized in the “How much OWL do you need to know to make sense of building ontologies?” paper presented at “LDAC2024 - Linked Data in Architecture and Construction” workshop. It also includes the resulting estructures and patterns identified as well as a library of graphical pattersn generated with the Chowlk notation (https://chowlk.linkeddata.es/).</p>
The Invasion Biology Ontology (INBIO)
<p>The Invasion Biology Ontology (INBIO) contains terms and concepts relevant in the field of invasion biology, which is a research area dealing with the translocation, establishment, spread, impact and management of species outside of their native ranges, where they are called non-native or alien species. This first version of the ontology covers terms and concepts needed to describe twelve major invasion hypotheses building the <a href="https://hi-knowledge.org/invasion-biology/">hierarchical hypothesis network</a> (see also Jeschke JM, Heger T (Eds) (2018) Invasion Biology: Hypotheses and Evidence. CABI, Wallingford, UK).</p>
Ontologizing Health Systems at Scale: Making Translational Discovery a Reality (Recorded talk)
<p>This entry contains the recorded talk that was presented at the 2021 American Medical Informatics Association Virtual Informatics Summit (<a href="https://amia.org/education-events/amia-2021-virtual-informatics-summit">https://amia.org/education-events/amia-2021-virtual-informatics-summit</a>).</p>
Additional Material for Architecture Smell Ontology
<p>This is the dataset of our literature review study on Architecture Smells. We provide the decisive exclusion criteria for excluded studies and the classification for included studies. We exported OWL file of the ontology from Protégé and included the tables of the architecture smells and the connected quality attributes and design principles. The study protocol is also included.</p> <p> </p>
Matching Network of Ontologies: a Pattern Recognition Approach
<p>Networks of Ontologies research deals with the need to combine several ontologies at the same time. In a world of integrated systems (system of systems), isolated systems are increasingly rare in the near future, and their integration creates opportunities to change, validate information and add more value to an information system. This system of systems can contain ontologies to support the corresponding knowledge model. Consequently, new integration requirements may have to deal with network alignment rather than single ontologies. This work delves into the area of network alignment and proposes new ways to approach a particular case of alignment of large ontologies. The contribution of the work is the use of algebraic operations on networks to eliminate candidates before alignment and to use a stochastic search method to discover the relevant nodes. These nodes should be retained as they increase accuracy and final alignment retrieval even though they are identical and removed by the algebraic operation. To find out the particular relevance of each node, we propose a random walk combined with a frequent itemsets approach that overcomes the force brute approaches in processing time, as the size of networks grows, and have close precision. The approach was validated using networks of ontologies created from the OAEI ontologies. The approach selected the entities to send to the matcher without losing significant preexisting alignments. Finally, two different matchers were used to get metrics and compare the results with the pairwise force brute approach.</p>
Requirements Quality Factor Ontology
<p>We investigated a previously published set of research articles concerned with requirements quality, extracted quality factors and other relevant elements from eligible publications, and iteratively constructed an ontology of quality factors for natural language requirements. The documentation of the process, the resulting data set, and a web application to visualize the results are contained in this artifact bundle.</p>
Biodiversity Metadata Ontology (BMO)
The Biodiversity Metadata Ontology (BMO) is an ontology that describes the underlying shared scheme of the metadata of seven Biodiversity data repositories. Such repositories include idiv, data.world, BEF-China, the Biodiversity Expolatores (BExIS), PANGEAE, Dryad, and gbif. We have manually collected metadata from them and analyzied such metadata. We have generated BMO using the <a href="https://github.com/fusion-jena/MakeSchema">MakeSchema</a> that is based on the python rdflib tool. We export the BMO to Turtle, RDF/XML, and n-triples format.
ClinSpEn-OC (Ontology Concepts) Test + Background Set
<p>This repository contains the test and background data for the ClinSpEn-Ontology Concepts sub-track. ClinSpEn is part of the Biomedical WMT 2022 shared task, having the aim to promote the development and evaluation of machine translation systems adapted to the medical domain with three highly relevant sub-tracks: clinical cases, medical controlled vocabularies/ontologies, and clinical terms and entities extracted from medical content.</p> <p>The data is made up of a TSV file with two columns: concept number and English concept. The direction of this sub-track is EN>ES. Ontologies and structured vocabularies represent a key resource for semantic interoperability, entity linking, biomedical knowledge bases and precision medicine, and thus there is a pressing need to generate multilingual biomedical ontologies for a range of clinical applications</p> <p> </p> <p>Related Links:</p> <p><strong>- Data website with more information: </strong><a href="https://temu.bsc.es/clinspen/">https://temu.bsc.es/clinspen/</a></p> <p><strong>- WMT website (includes schedule, registration, ...): </strong><a href="https://www.statmt.org/wmt22/">https://www.statmt.org/wmt22/</a></p> <p><strong>- CodaLab: </strong><a href="https://codalab.lisn.upsaclay.fr/competitions/6696">https://codalab.lisn.upsaclay.fr/competitions/6696</a></p> <p> </p> <p>ClinSpEn SAMPLE SETS:</p> <p><strong>- ClinSpEn-CC Sample Set (Clinical Cases):</strong> <a href="https://doi.org/10.5281/zenodo.6497350">https://doi.org/10.5281/zenodo.6497350</a></p> <p><strong>- ClinSpEn-CT Sample Set (Clinical Terms): </strong><a href="https://doi.org/10.5281/zenodo.6497372">https://doi.org/10.5281/zenodo.6497372</a></p> <p><strong>- ClinSpEn-OC Sample Set (Ontology Concepts): </strong><a href="https://doi.org/10.5281/zenodo.6497388">https://doi.org/10.5281/zenodo.6497388</a></p> <p>ClinSpEn TEST SETS:</p> <p><strong>- ClinSpEn-CC Test Set (Clinical Cases): </strong><a href="https://doi.org/10.5281/zenodo.6948634">https://doi.org/10.5281/zenodo.6948634</a></p> <p><strong>- ClinSpEn-CT Test Set (Clinical Terms): </strong><a href="https://doi.org/10.5281/zenodo.6948669">https://doi.org/10.5281/zenodo.6948669</a></p> <p><strong>- ClinSpEn-OC Test Set (Ontology Concepts): </strong><a href="https://doi.org/10.5281/zenodo.6948679">https://doi.org/10.5281/zenodo.6948679</a></p> <p> </p>
Seasonal and ontological variation in diet and age-related differences in prey choice, by an insectivorous songbird
<p>The diet of an individual animal is subject to change over time, both in response to short-term food fluctuations and over longer time scales as an individual ages and meets different challenges over its life cycle. A metabarcoding approach was used to elucidate the diet of different life stages of a migratory songbird, the Eurasian reed warbler (<em>Acrocephalus scirpaceus</em>) over the 2017 summer breeding season in Somerset, UK. The faeces of adult, juvenile and nestling warblers were screened for invertebrate DNA, enabling the identification of prey species. Dietary analysis was coupled with monitoring of Diptera in the field using yellow sticky traps. Seasonal changes in warbler diet were subtle whereas age class had a greater influence on overall diet composition. Age classes showed high dietary overlap, but significant dietary differences were mediated through the selection of prey; i) from different taxonomic groups, ii) with different habitat origins (aquatic versus terrestrial) and iii) of different average approximate sizes. Our results highlight the value of metabarcoding data for enhancing ecological studies of insectivores in dynamic environments. </p>
Figure 4: ExaminationDB Ontology class and subclass
<p>DB Mapping: a case of study<br> In order to explain how the methodology works, we analyze step by step,<br> a simple mapping on a database sample named ExaminationDB .<br> STEP 1. Seven classes are generated: assistant_teacher, course_degree,<br> examination, professor, relative, student, sustains.<br> STEP 2. Doctor and researcher are added as a set of disjoined subclasses<br> of class assistant_teacher; associate_professor and full_professor are added<br> as a set of disjoined subclasses of class professor (Figure 4).<br> STEP 3. 32 Data Properties are generated : 11 related to the primary<br> keys (named PK_NomeDominio_NomeAttributo and 21 related to the others<br> attributes and named NomeDominio_NomeAttributo. For each data type of a<br> table attribute, exists a range of values in the corresponding Data Property in the generated ontology. For example the Data Property professor_Address is<br> a string, because the attribute address of the professor table in ExaminationDb<br> is a Varchar type.<br> STEP 4. 10 Object Properties are generated . Six Object Properties are<br> related to the Foreign Keys (examinationhasFK_Cod_Course, examinationhasFK_<br> CF_PROFESSOR etc. . . ). Four Object Properties are related to the<br> class involved in disjoint relations (associate_professor, full_professor, doctor,<br> researcher), and they have the parent class as a Range and the primary key<br> as a relation key. For example we have the 4 object Properties: researcherhasPK_<br> CF, associate_professorhasPK_CF, full_professorhasPK_CF, doctorhasPK_<br> CF.<br> STEP 5. It is created a restriction having a minimum cardinality equal<br> to 1 or to 0, on each Data Property corresponding respectively to a NOT<br> NULL (avoiding the PK) and to a NULL attribute. For example we will have<br> assistant_teacher_Address min 1;<br> STEP 6. On each Data Property corresponding to a Primary Key attribute<br> of the database source, is added a restriction having cardinality exactly 1.</p>
Figure 2: Site's Ontology
<p>Structuring Layer: it must generate a new ontology reflecting a particular website structure, and it must merge it with the DB Ontology. The Structuring Layer is composed by two sublayers: (i) the Site Mapping Sublayer map the website structure over a domain ontology that we will call Site Structure Ontology; (ii) the Merging Layer merge the DB Ontology with the Site Structure Ontology creating a new ontology called Site’s Ontology.</p>
Figure 1. The upper ontology of sorts in MultiNet (after Helbig [17])-Representing Mental Spaces and Dynamics of Natural Language Semantics
<p>One of the distinguishing features of MultiNet is its commitment to the Cognitive Adequacy<br> requirement. According this requirement [9], semantic representations and knowledge<br> representations should be centered around concepts. Concepts2 are represented by nodes in the<br> graphical representation of the network. Every node belongs to a specific sort defined by the<br> MultiNet’s ontology of sorts (Figure 2).</p>
Planteome/CO_340-cowpea-traits: CO_340-cowpea-traits ontology
<p>Cowpea Trait Dictionary - IITA - August 2015 - Updated Nov 2023 with the traits and variables for on farm comparative ranking of varieties</p>
Planteome/CO_370-apple-traits: Apple Trait Ontology v1.0
<p>This is the first release of the Apple Trait Ontology that is published online the <a href="https://cropontology.org/">Crop Ontology Web site</a></p>
OntoPortal ontologies
<p>Snapshot of the OntoPortal ontologies on 2024-05-11; the dataset was used for the metric-based ontology analysis (evaluome-on-ontoportal)</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.